Breast cancer is one of the most frequently diagnosed cancers today and the second leading cause of cancer related death among women. One indicator for predicting clinical behavior and prognosis of patients with breast cancer is the histological examination of biopsy/surgical samples based on a qualitative and semi-quantitative visual examination of sectioned tissue samples stained with immunohistochemical (IHC) markers, such as histological stains that provide the ability to differentiate microscopic structures of interest. Biomarkers can be used to characterize the tumor and identify the most appropriate treatment and medication that can improve the clinical outcome.
As opposed to membrane biomarkers, nuclear biomarkers interact with proteins in cell nuclei and dye cell nuclei. The color of a stained cell is indicative of the antigen (biomarker)-antibody binding for the cell. In a clinical reading, pathologists often report a score for the slide by visually reviewing and estimating the percentage of positively-stained (e.g., brown-colored) nuclear objects to the total number of positively-stained and negatively-stained (e.g., blue-colored) nuclear objects. In clinical and laboratory settings, a precise measurement requires manual counting of tumor cells by identifying positively-stained tumor cells, which can be extremely tedious. In practice, the slide score is often based on a “guestimation” by the pathologist. As a result, the manual score is not reproducible and is further subject to significant inter- and intra-reader variability. Moreover, for practical reasons, the interpretation of a whole slide is based only on a few representative fields of view (FOVs) identified by the pathologists, and the information in those fields of view only. Unfortunately, this “representative” analysis can lead to sampling bias.